Methods for improving signal quality in wearable biometric monitoring devices

ABSTRACT

A wearable biometric monitoring device is configured to assess the biometric signal quality of one or more sensors associated with the monitoring device, determine how the user should adjust the device to improve the biometric fit, and instruct the user to wear the biometric monitoring device a certain way. Communicating instructions to a user may include instructing the user to execute a testing regimen while wearing the biometric monitoring device. The testing regimen facilitates an estimation of a signal quality that can be used to provide feedback to the user that he/she needs to adjust the device to improve the biometric fit and the biometric signal quality.

RELATED APPLICATIONS

This application is a divisional application of pending U.S. patentapplication Ser. No. 14/829,032, filed Aug. 18, 2015, which claims thebenefit of and priority to U.S. Provisional Patent Application No.62/056,510 filed Sep. 27, 2014, and U.S. Provisional Patent ApplicationNo. 62/110,655 filed Feb. 2, 2015, the disclosures of which areincorporated herein by reference as if set forth in their entireties.

FIELD OF THE INVENTION

The present invention relates generally to monitoring devices andmethods, more particularly, to monitoring devices and methods formeasuring physiological information.

BACKGROUND OF THE INVENTION

Photoplethysmography (PPG) is based upon shining light into the humanbody and measuring how the scattered light intensity changes with eachpulse of blood flow. The scattered light intensity will change in timewith respect to changes in blood flow or blood opacity associated withheart beats, breaths, blood oxygen level (SpO₂), and the like. Such asensing methodology may require the magnitude of light energy reachingthe volume of flesh being interrogated to be steady and consistent sothat small changes in the quantity of scattered photons can beattributed to varying blood flow. If the incidental and scattered photoncount magnitude changes due to light coupling variation between thesource or detector and the skin or other body tissue, then the signal ofinterest can be difficult to ascertain due to large photon countvariability caused by motion artifacts. Changes in the surface area (andvolume) of skin or other body tissue being impacted with photons, orvarying skin surface curvature reflecting significant portions of thephotons may also significantly impact optical coupling efficiency.Physical activity, such as walking, cycling, running, etc., may causemotion artifacts in the optical scatter signal from the body, andtime-varying changes in photon intensity due to motion artifacts mayswamp-out time-varying changes in photon intensity due to blood flowchanges. Environmental artifacts, such as ambient light noise, as wellas motion-coupled ambient light noise can further swamp-out blood-flowrelated signals. Each of these changes in optical coupling candramatically reduce the signal-to-noise ratio (S/N) of biometric PPGinformation to total time-varying photonic interrogation count. This canresult in a much lower accuracy in metrics derived from PPG data, suchas heart rate and breathing rate.

The signal quality from a biometric sensor, such as a PPG sensor, in awearable monitoring device increases when the monitoring device is worncorrectly and decreases when the monitoring device is worn incorrectly.For example, a user may go for a run with a biometric earbud and expectaccurate heart rate zone information from the sensor(s) therein, only tofind the sensor data is erroneous due to a poor fitting of the biometricearbud within the ear. Unfortunately, without some way to measure signalquality, a user may not know if sensor signal quality is adequate.

SUMMARY

It should be appreciated that this Summary is provided to introduce aselection of concepts in a simplified form, the concepts being furtherdescribed below in the Detailed Description. This Summary is notintended to identify key features or essential features of thisdisclosure, nor is it intended to limit the scope of the invention.

According to some embodiments of the present invention, a wearablebiometric monitoring device is configured to assess the biometric signalquality of one or more sensors associated with the biometric monitoringdevice, determine how the user should adjust the device to improve thebiometric fit, and instruct the user to wear the device a certain way(e.g., audio-visually via a remote device). In some embodiments, thismay involve an iterative approach. In some embodiments, communicatinginstructions to a user may include instructing the user to execute atesting regimen while wearing the device. The testing regimenfacilitates an estimation of signal quality that can be used to providefeedback to the user that he/she needs to adjust the device to improvethe biometric fit (and hence the biometric signal quality).

According to some embodiments of the present invention, a method ofmonitoring signal quality of a wearable biometric monitoring devicehaving at least one sensor configured to detect and/or measurephysiological information from a subject wearing the biometricmonitoring device and at least one processor in communication with theat least one sensor that is configured to receive and analyze signalsproduced by the at least one sensor includes instructing the subject viathe at least one processor to begin an exercise regimen, measuringsignal quality produced by the at least one sensor during the exerciseregimen, and communicating information to the subject regarding thesignal quality during the exercise regimen. In some embodiments, thebiometric monitoring device is configured to be integrated within anearbud. In some embodiments, the biometric monitoring device isconfigured to be integrated within an audio headset, a wrist strap, awrist watch, an ankle bracelet, an armband, etc. In other embodiments,the biometric monitoring device comprises a band that configured to atleast partially encircle a portion of the body of a subject, such as alimb, a nose, an earlobe, and/or a digit, etc.

In some embodiments, instructing the subject to begin an exerciseregimen comprises sending an audio and/or visual communication to aremote device (e.g., a smartphone, computer, etc.) in communication withthe biometric monitoring device. In other embodiments, instructing thesubject to begin an exercise regimen comprises causing the biometricmonitoring device and/or a remote device to vibrate.

Communicating information to the subject regarding the signal qualitymay include communicating instructions to the subject to adjust thebiometric monitoring device relative to the body of the subject if thesignal quality is below a threshold level. Communicating information tothe subject regarding the signal quality also may include communicatinginformation to the subject that the signal quality is above a thresholdlevel.

In some embodiments, communicating information to the subject regardingthe signal quality comprises sending an audio and/or visualcommunication to a remote device in communication with the biometricmonitoring device. In other embodiments, communicating information tothe subject regarding the signal quality comprises causing the biometricmonitoring device and/or a remote device to vibrate.

In some embodiments, the biometric monitoring device includes anactuator, and the method further comprises automatically adjusting thebiometric monitoring device relative to the body of the subject via theactuator if the signal quality is below a threshold level.

In some embodiments, the biometric monitoring device includes a motionsensor, and the method further comprises determining if the subject hasbegun the exercise regimen by detecting a change in subject activity viathe motion sensor.

In some embodiments, the biometric monitoring device includes a motionsensor, and the method further comprises determining if the subject iswearing the biometric monitoring device by determining if body motionover a period of time is above a threshold.

According to some embodiments of the present invention, a method ofgenerating a physiological assessment of a subject includes collectingphysiological information and/or motion information from the subject viaat least one wearable device having at least one physiological sensorand/or at least one motion sensor, determining a quality level for thephysiological information and/or motion information at one or moreselected times during the period of time, and generating a physiologicalassessment for the subject using the physiological information and/ormotion information at the one or more selected times that has a qualitylevel above a threshold level.

According to some embodiments of the present invention, a method ofgenerating a physiological assessment of a subject includes collectingphysiological information and/or motion information from a subject viaat least one wearable device having at least one physiological sensorand/or at least one motion sensor, determining one or more time periodswhen the wearable device is being worn by the subject, and generating aphysiological assessment for the subject using the physiologicalinformation and/or motion information obtained during the one or moretime periods when the wearable device is being worn by the subject.

According to some embodiments of the present invention, a method ofdetecting if a biometric monitoring device having a PPG sensor is beingworn by a subject includes processing data produced by the PPG sensorvia at least one processor to determine one or more of the following:whether intensity of a DC component of a PPG signal from the PPG sensoris within a predetermined range, whether at least one vital sign of thesubject is detected, and whether a heart rate value of the subject iswithin a predetermined range. In some embodiments, the processor maygenerate an indication as to whether or not the biometric monitoringdevice is being worn by the subject. In some embodiments, the biometricmonitoring device is integrated within an earbud, an audio headset, awrist strap, a wrist watch, an ankle bracelet, or an armband. In someembodiments, the biometric monitoring device comprises a band configuredto at least partially encircle a portion of the body of a subject, andwherein the portion of the body comprises a limb, a nose, an earlobe,and/or a digit.

According to some embodiments of the present invention, a method ofdetecting if a biometric monitoring device having a PPG sensor and atleast one processor is being worn by a subject includes determiningquality of a signal produced by the PPG sensor, making an estimate as towhether the biometric monitoring device is being worn based on thesignal produced by the PPG sensor, and determining whether the biometricmonitoring device is being worn by processing the signal in context withthe quality of the signal. In some embodiments, the at least oneprocessor may generate an indication as to whether or not the biometricmonitoring device is being worn by the subject. In some embodiments, thebiometric monitoring device is integrated within an earbud, an audioheadset, a wrist strap, a wrist watch, an ankle bracelet, or an armband.In some embodiments, the biometric monitoring device comprises a bandconfigured to at least partially encircle a portion of the body of asubject, and wherein the portion of the body comprises a limb, a nose,an earlobe, and/or a digit.

According to some embodiments of the present invention, a method ofdetecting if a biometric monitoring device having a motion sensor and atleast one processor is being worn by a subject includes determiningquality of a signal produced by the motion sensor, making an estimate asto whether the biometric monitoring device is being worn based on thesignal produced by the motion sensor, and determining whether thebiometric monitoring device is being worn by processing the signal incontext with the quality of the signal. In some embodiments, the atleast one processor may generate an indication as to whether or not thebiometric monitoring device is being worn by the subject. In someembodiments, the biometric monitoring device is integrated within anearbud, an audio headset, a wrist strap, a wrist watch, an anklebracelet, or an armband. In some embodiments, the biometric monitoringdevice comprises a band configured to at least partially encircle aportion of the body of a subject, and wherein the portion of the bodycomprises a limb, a nose, an earlobe, and/or a digit.

According to some embodiments of the present invention, a method ofmonitoring signal quality of a wearable biometric monitoring device isprovided. The biometric monitoring device includes at least onephysiological sensor (e.g., a PPG sensor, etc.) configured to detectphysiological and at least one sensor (e.g., an accelerometer, etc.)configured to detect motion information from a subject wearing thebiometric monitoring device and at least one processor in communicationwith the at least one sensor that is configured to receive and analyzesignals produced by the at least one sensor. The method includesmeasuring quality of a signal produced by the physiological sensorduring an exercise regimen that includes factoring an amount ofphysiological information in the signal in comparison with an amount ofmotion information in the signal. Factoring the amount of physiologicalinformation in the signal in comparison with the amount of motioninformation in the signal includes calculating a ratio of physiologicalinformation and motion information.

It is noted that aspects of the invention described with respect to oneembodiment may be incorporated in a different embodiment although notspecifically described relative thereto. That is, all embodiments and/orfeatures of any embodiment can be combined in any way and/orcombination. Applicant reserves the right to change any originally filedclaim or file any new claim accordingly, including the right to be ableto amend any originally filed claim to depend from and/or incorporateany feature of any other claim although not originally claimed in thatmanner. These and other objects and/or aspects of the present inventionare explained in detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which form a part of the specification,illustrate various embodiments of the present invention. The drawingsand description together serve to fully explain embodiments of thepresent invention.

FIG. 1 is a block diagram of a wearable biometric monitoring device thatcan communicate with a remote device, such as a smartphone, and that canassess the biometric signal quality of one or more sensors associatedwith the device, according to some embodiments of the present invention.

FIGS. 2A-2B illustrate a biometric monitoring device that can bepositioned within an ear of a subject, according to some embodiments ofthe present invention.

FIG. 3A illustrates a biometric monitoring device that can be positionedaround an appendage of the body of a subject, according to someembodiments of the present invention.

FIG. 3B is a cross sectional view of the biometric monitoring device ofFIG. 3A.

FIG. 4 is a flowchart of operations for monitoring and improving signalquality of a wearable biometric monitoring device, according to someembodiments of the present invention.

FIGS. 5A-5B, 6A-6B, 7A-7B and 8A-8B illustrate communications sent to auser in the form of graphical illustrations, and that can facilitate theproper positioning of a monitoring device, according to some embodimentsof the present invention.

FIG. 9 is a graphical illustration of a time-domain PPG waveformproduced by a wearable biometric monitoring device according to someembodiments of the present invention and where there are negligiblemotion artifacts associated with the waveform.

FIG. 10 is a graphical illustration of a time-domain PPG waveformproduced by a wearable biometric monitoring device according to someembodiments of the present invention and where there are substantialmotion artifacts associated with the waveform.

FIG. 11A illustrates an exemplary spectrogram of a PPG signal from auser wearing a biometric monitoring device having a PPG sensor accordingto some embodiments of the present invention and without active motionnoise removal.

FIG. 11B illustrates the removal of motion noise from the spectrogram ofFIG. 11A via one or more filters.

FIG. 12 is an exemplary plot of signal quality Qs from a sensor of abiometric monitoring device over time for an exemplary run by a userwearing the biometric monitoring device according to some embodiments ofthe present invention.

FIG. 13 is a plot of signal quality Qs from a sensor of a biometricmonitoring device over time for a user conducting a self-fitting testfor the biometric monitoring device, according to some embodiments ofthe present invention.

FIG. 14 is a plot of signal quality Qs from a sensor of a biometricmonitoring device over time for a user conducting an instructed “quicktest” regimen for the biometric monitoring device, according to someembodiments of the present invention.

FIG. 15 is a flowchart of operations for generating biometricassessments of subjects wearing a biometric monitoring device, accordingto some embodiments of the present invention.

FIG. 16 illustrates heart rate data over time produced by both highquality and low quality sensor signals of a wearable biometricmonitoring device.

FIG. 17 is a plot of HRR (Heart Rate Recovery) over time for a user andalso includes information about the confidence of HRR score for eachdata point.

FIG. 18 is a flowchart of operations for generating biometricassessments of subjects only when a biometric monitoring device is beingworn, according to some embodiments of the present invention.

FIGS. 19-20 are flowcharts of operations for detecting that a monitoringdevice is being worn by a subject, according to some embodiments of thepresent invention.

DETAILED DESCRIPTION

The present invention will now be described more fully hereinafter withreference to the accompanying figures, in which embodiments of theinvention are shown. This invention may, however, be embodied in manydifferent forms and should not be construed as limited to theembodiments set forth herein. Like numbers refer to like elementsthroughout. In the figures, certain layers, components or features maybe exaggerated for clarity, and broken lines illustrate optionalfeatures or operations unless specified otherwise. In addition, thesequence of operations (or steps) is not limited to the order presentedin the figures and/or claims unless specifically indicated otherwise.Features described with respect to one figure or embodiment can beassociated with another embodiment or figure although not specificallydescribed or shown as such.

It will be understood that when a feature or element is referred to asbeing “on” another feature or element, it can be directly on the otherfeature or element or intervening features and/or elements may also bepresent. In contrast, when a feature or element is referred to as being“directly on” another feature or element, there are no interveningfeatures or elements present. It will also be understood that, when afeature or element is referred to as being “secured”, “connected”,“attached” or “coupled” to another feature or element, it can bedirectly secured, directly connected, attached or coupled to the otherfeature or element or intervening features or elements may be present.In contrast, when a feature or element is referred to as being “directlysecured”, “directly connected”, “directly attached” or “directlycoupled” to another feature or element, there are no interveningfeatures or elements present. Although described or shown with respectto one embodiment, the features and elements so described or shown canapply to other embodiments.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise.

As used herein, the terms “comprise”, “comprising”, “comprises”,“include”, “including”, “includes”, “have”, “has”, “having”, or variantsthereof are open-ended, and include one or more stated features,integers, elements, steps, components or functions but does not precludethe presence or addition of one or more other features, integers,elements, steps, components, functions or groups thereof. Furthermore,as used herein, the common abbreviation “e.g.”, which derives from theLatin phrase “exempli gratia,” may be used to introduce or specify ageneral example or examples of a previously mentioned item, and is notintended to be limiting of such item. The common abbreviation “i.e.”,which derives from the Latin phrase “id est,” may be used to specify aparticular item from a more general recitation.

As used herein, the term “and/or” includes any and all combinations ofone or more of the associated listed items and may be abbreviated as“/”.

As used herein, phrases such as “between X and Y” and “between about Xand Y” should be interpreted to include X and Y. As used herein, phrasessuch as “between about X and Y” mean “between about X and about Y.” Asused herein, phrases such as “from about X to Y” mean “from about X toabout Y.”

Spatially relative terms, such as “under”, “below”, “lower”, “over”,“upper” and the like, may be used herein for ease of description todescribe one element or feature's relationship to another element(s) orfeature(s) as illustrated in the figures. It will be understood that thespatially relative terms are intended to encompass differentorientations of the device in use or operation in addition to theorientation depicted in the figures. For example, if a device in thefigures is inverted, elements described as “under” or “beneath” otherelements or features would then be oriented “over” the other elements orfeatures. Thus, the exemplary term “under” can encompass both anorientation of over and under. The device may be otherwise oriented(rotated 90 degrees or at other orientations) and the spatially relativedescriptors used herein interpreted accordingly. Similarly, the terms“upwardly”, “downwardly”, “vertical”, “horizontal” and the like are usedherein for the purpose of explanation only unless specifically indicatedotherwise.

It will be understood that although the terms first and second are usedherein to describe various features or elements, these features orelements should not be limited by these terms. These terms are only usedto distinguish one feature or element from another feature or element.Thus, a first feature or element discussed below could be termed asecond feature or element, and similarly, a second feature or elementdiscussed below could be termed a first feature or element withoutdeparting from the teachings of the present invention.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this invention belongs. It will befurther understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the specification andrelevant art and should not be interpreted in an idealized or overlyformal sense unless expressly so defined herein. Well-known functions orconstructions may not be described in detail for brevity and/or clarity.

The term “about”, as used herein with respect to a value or number,means that the value or number can vary more or less, for example by+/−20%, +/−10%, +/−5%, +/−1%, +/−0.5%, +/−0.1%, etc.

The terms “sensor”, “sensing element”, and “sensor module”, as usedherein, are interchangeable and refer to a sensor element or group ofsensor elements that may be utilized to sense information, such asinformation (e.g., physiological information, body motion, etc.) fromthe body of a subject and/or environmental information in a vicinity ofthe subject. A sensor/sensing element/sensor module may comprise one ormore of the following: a detector element, an emitter element, aprocessing element, optics, mechanical support, supporting circuitry,and the like. Both a single sensor element and a collection of sensorelements may be considered a sensor, a sensing element, or a sensormodule.

The term “optical emitter”, as used herein, may include a single opticalemitter and/or a plurality of separate optical emitters that areassociated with each other.

The term “optical detector”, as used herein, may include a singleoptical detector and/or a plurality of separate optical detectors thatare associated with each other.

The term “wearable sensor module”, as used herein, refers to a sensormodule configured to be worn on or near the body of a subject.

The terms “monitoring device”, “biometric monitoring device” and“biometric monitor”, as used herein, are interchangeable and include anytype of device, article, or clothing that may be worn by and/or attachedto a subject and that includes at least one sensor/sensingelement/sensor module. Exemplary monitoring devices may be embodied inan earpiece, a headpiece, a finger clip, a digit (finger or toe) piece,a limb band (such as an arm band or leg band), an ankle band, a wristband, a nose piece, a sensor patch, eyewear (such as glasses or shades),apparel (such as a shirt, hat, underwear, etc.), a mouthpiece or toothpiece, contact lenses, or the like.

The term “monitoring” refers to the act of measuring, quantifying,qualifying, estimating, sensing, calculating, interpolating,extrapolating, inferring, deducing, or any combination of these actions.More generally, “monitoring” refers to a way of getting information viaone or more sensing elements. For example, “blood health monitoring”includes monitoring blood gas levels, blood hydration, andmetabolite/electrolyte levels.

The term “headset”, as used herein, is intended to include any type ofdevice or earpiece that may be attached to or near the ear (or ears) ofa user and may have various configurations, without limitation. Headsetsincorporating biometric monitoring devices, as described herein, mayinclude mono headsets (a device having only one earbud, one earpiece,etc.) and stereo headsets (a device having two earbuds, two earpieces,etc.), earbuds, hearing aids, ear jewelry, face masks, headbands, andthe like. In some embodiments, the term “headset” may include broadlyheadset elements that are not located on the head but are associatedwith the headset. For example, in a “medallion” style wireless headset,where the medallion comprises the wireless electronics and theheadphones are plugged into or hard-wired into the medallion, thewearable medallion would be considered part of the headset as a whole.Similarly, in some cases, if a mobile phone or other mobile device isintimately associated with a plugged-in headphone, then the term“headset” may refer to the headphone-mobile device combination. Theterms “headset” and “earphone”, as used herein, are interchangeable.

The term “physiological” refers to matter or energy of or from the bodyof a creature (e.g., humans, animals, etc.). In embodiments of thepresent invention, the term “physiological” is intended to be usedbroadly, covering both physical and psychological matter and energy ofor from the body of a creature.

The term “body” refers to the body of a subject (human or animal) thatmay wear a monitoring device, according to embodiments of the presentinvention.

The term “processor” is used broadly to refer to a signal processor orcomputing system or processing or computing method which may belocalized or distributed. For example, a localized signal processor maycomprise one or more signal processors or processing methods localizedto a general location, such as to a wearable device. Examples of suchwearable devices may comprise an earpiece, a headpiece, a finger clip, adigit (finger or toe) piece, a limb band (such as an arm band or legband), an ankle band, a wrist band, a nose piece, a sensor patch,eyewear (such as glasses or shades), apparel (such as a shirt, hatunderwear, etc.), a mouthpiece or tooth piece, contact lenses, or thelike. Examples of a distributed processor comprise “the cloud”, theinternet, a remote database, a remote processor computer, a plurality ofremote processors or computers in communication with each other, or thelike, or processing methods distributed amongst one or more of theseelements. The key difference is that a distributed processor may includedelocalized elements, whereas a localized processor may workindependently of a distributed processing system. As a specific example,microprocessors, microcontrollers, ASICs (application specificintegrated circuit), analog processing circuitry, or digital signalprocessors are a few non-limiting examples of physical signal processorsthat may be found in wearable devices.

The term “remote” does not necessarily mean that the “remote device” isa wireless device or that it is a long distance away from a device incommunication with a “remote device”. Rather, the term “remote” is usedto reference a device or system that is distinct from another device orsystem or that is not substantially reliant on another device or systemfor core functionality. For example, a computer wired to a wearabledevice may be considered a remote device, as the two devices aredistinct and/or not substantially reliant on each other for corefunctionally. However, any wireless device (such as a portable device,for example) or system (such as a remote database for example) isconsidered remote to any other wireless device or system.

The term “RRi” refers to “R-R interval” in the electrocardiogram orphotoplethysmogram of a person. Generally, where heart rate is used inembodiments of the present invention, RRi may also be applied in asimilar manner. However, RRi and heart rate are generally related in aninverse fashion, such that 1/RRi=instantaneous heart rate.

The term “HRV” refers to “heart rate variability” or “R-R variability”,which is a statistical representation of a group of consecutive R-Rintervals or N-N intervals (beat-to-beat intervals between consecutiveheart beats). The types of statistics performed to generate an HRV valuecan be quite numerous and broad. In general, a variety of differenttime-domain and/or frequency domain statistics on heart beat intervalscan be described as different HRV values. As one specific example ofHRV, 2- or 5-minutes worth of R-R intervals may be processed todetermine the mean and standard deviation (SDNN), which is arepresentation of HRV. In general, the higher the SDNN for a group ofR-R intervals collected from a person, the more relaxed, physically fit,or healthy that person may be. N-N intervals may be collected viaphotoplethysmograms (PPG), electrocardiograms (ECG), blood pressurepulses, ballistocardiograms (BCG), and the like.

In the following figures, various monitoring devices will be illustratedand described for attachment to the ear or an appendage of the humanbody. However, it is to be understood that embodiments of the presentinvention are not limited to the illustrated monitoring devices or tothose worn by humans.

The ear is an ideal location for wearable health and environmentalmonitors. The ear is a relatively immobile platform that does notobstruct a person's movement or vision. Monitoring devices located at anear have, for example, access to the inner-ear canal and tympanicmembrane (for measuring core body temperature), muscle tissue (formonitoring muscle tension), the pinna, earlobe, and elsewhere (formonitoring blood gas levels), the region behind the ear (for measuringskin temperature and galvanic skin response), and the internal carotidartery (for measuring cardiopulmonary functioning), etc. A particularlydistinct pulsatile blood flow waveform can be discerned optically, viaPPG, between the anti-tragus and concha region of the ear. The ear isalso at or near the point of exposure to: environmental breathabletoxicants of interest (volatile organic compounds, pollution, etc.);noise pollution experienced by the ear; and lighting conditions for theeye. Furthermore, as the ear canal is naturally designed fortransmitting acoustical energy, the ear provides a good location formonitoring internal sounds, such as heartbeat, breathing rate, and mouthmotion.

FIG. 1 is a schematic diagram of a wearable monitoring device 10,according to some embodiments of the present invention. The wearablebiometric monitoring device 10 may be an earbud module configured to bepositioned within the ear of a subject, may be in the form of a sensorband configured to be secured to an appendage (e.g., an arm, wrist,hand, finger, toe, leg, foot, neck, etc.) of a subject, may be worninternally in the body (e.g., within the mouth as with a mouth guard,etc.), may be a sensor device configured to be adhesively secured to anyportion of the body of a subject. Wearable monitoring devices, accordingto some embodiments of the present invention, may also be integratedwithin an audio headset, a wrist strap, a wrist watch, an anklebracelet, a headband, an armband, etc. Wearable monitoring devices,according to some embodiments of the present invention, may also beutilized in various devices and articles including, but not limited to,patches, clothing, etc. Embodiments of the present invention can beutilized wherever PPG and blood flow signals can be obtained and at anylocation on the body of a subject. Embodiments of the present inventionare not limited to the illustrated monitoring devices.

FIGS. 2A-2B illustrate a monitoring apparatus 20 configured to besecured to an ear of a subject and that may function as the monitoringdevice 10 of FIG. 1, according to some embodiments of the presentinvention. The illustrated apparatus 20 includes an earpiece body orhousing 22, a sensor module 24, a stabilizer 25, and a sound port 26.When positioned within the ear of a subject, the sensor module 24 has aregion 24 a configured to contact a selected area of the ear. Theillustrated sensor region 24 a may be contoured (i.e., is “form-fitted”)to matingly engage a portion of the ear between the anti tragus andacoustic meatus, and the stabilizer is configured to engage theanti-helix. However, monitoring devices in accordance with embodimentsof the present invention can have sensor modules with one or moreregions configured to engage various portions of the ear. Various typesof device configured to be worn at or near the ear may be utilized inconjunction with embodiments of the present invention.

FIGS. 3A-3B illustrate a monitoring apparatus 30 in the form of a sensorstrap or band 32 configured to be secured to an appendage (e.g., an arm,wrist, hand, finger, toe, leg, foot, neck, etc.) of a subject and thatmay function as the monitoring device 10 of FIG. 1, according to someembodiments of the present invention. The band 32 includes a sensormodule 34 on or extending from the inside surface 32 a of the band 32.The sensor module 34 is configured to detect and/or measurephysiological information from the subject and includes a sensor region34 a that may be contoured to contact the skin of a subject wearing theapparatus 30.

Embodiments of the present invention may be utilized in various devicesand articles including, but not limited to, patches, clothing, etc.Embodiments of the present invention can be utilized wherever PPG andblood flow signals can be obtained and at any location on the body of asubject. Embodiments of the present invention are not limited to theillustrated monitoring devices.

The sensor modules 24, 34 for the illustrated monitoring devices 20, 30of FIGS. 2A-2B and 3A-3B are configured to detect and/or measurephysiological information from the subject. In some embodiments, thesensor modules 24, 34 may be configured to detect and/or measure one ormore environmental conditions in a vicinity of the subject wearing themonitoring device 20, 30.

A sensor module utilized in accordance with embodiments of the presentinvention may be an optical sensor module that includes at least oneoptical emitter and at least one optical detector. Exemplary opticalemitters include, but are not limited to light-emitting diodes (LEDs),laser diodes (LDs), organic light-emitting diodes (OLEDs), compactincandescent bulbs, micro-plasma emitters, IR blackbody sources, or thelike. In addition, a sensor module may include various types of sensorsincluding and/or in addition to optical sensors. For example, a sensormodule may include one or more inertial sensors (e.g., an accelerometer,piezoelectric sensor, vibration sensor, photoreflector sensor, etc.) fordetecting changes in motion, one or more thermal sensors (e.g., athermopile, thermistor, resistor, etc.) for measuring temperature of apart of the body, one or more electrical sensors for measuring changesin electrical conduction, one or more skin humidity sensors, and/or oneor more acoustical sensors.

Referring back to FIG. 1, various components of a wearable biometricmonitoring device 10, according to embodiments of the present invention,are illustrated. The biometric monitoring device 10 includes one or moresensors 12 (e.g., one or more sensors in the sensor regions 24, 34 ofthe devices in FIGS. 2A-2B and 3A-3B, etc.), and at least one processor14 that is coupled to the sensor(s) 12 and that is configured to receiveand analyze signals produced by the sensor(s). The illustrated biometricmonitoring device 10 also includes a communication component 16 and anadjustment mechanism 18.

The communication component 16 allows the processor(s) 14 to communicatewith a wearer of the biometric monitoring device 10 via a remote device40, such as a smart phone, computer, etc. In some embodiments, thecommunication component 16 and processor component 14 may be integratedtogether, such as with a wireless processor, such as a Bluetoothchipset, WiFi chipset, ZigBee chipset, or the like.

The adjustment mechanism 18 may be any type of device that facilitatesproper placement of the biometric monitoring device 10 relative to thebody of the subject. Exemplary adjustment mechanisms 18 may include, butare not limited to, actuators, spacers, padding, straps, ear geldevices, ear stabilization pieces (such as pieces designed to fit withinregions of the ear to stabilize an earbud within the ear), adjustmentholes (such as the holes used to adjust a belt or wristband on the bodyvia a bar or wedge that fits in the holes and secures the belt or band),ratcheting mechanism(s), spring mechanisms (such as structure thatcompresses a part of the housing or sensor region to the body of asubject), threaded mechanisms (such as a jackscrew, etc.), fluidcompression mechanisms, air pumping mechanisms, etc. In some cases, theadjustment mechanism may be autonomous and may not require physicaladjustment by the subject wearing the device, for example, as describedin PCT Application No. US 2015/042636, which is incorporated herein byreference in its entirety.

It should be understood that the wearable device 10 may comprise all oronly part of the illustrated components (i.e., sensor(s) 12,processor(s) 14, communication component(s) 16, or adjustment mechanism18). In the case where the wearable device 10 comprises only some ofthese components, the functionality of the remaining components may berealized all or in part by a remote device 40. The remote device 40 maybe in wired or wireless communication with the wearable device 10.Non-limiting examples of such remote devices 40 may include a smartphoneor other type of phone, a sensor hub, a computer, a smart pad, acloud-based processing system, a wearable accessory, a control box, a“medallion”, a smartwatch, smart glasses, etc.

Referring now to FIG. 4, operations for adjusting signal quality of oneor more sensors of a wearable biometric monitoring device 10, accordingto some embodiments of the present invention, are illustrated.Initially, the wearer of a biometric monitoring device 10 is instructedvia the processor(s) 14 to initiate a signal quality test (Block 100).The signal quality test may involve various activities that result inmotion noise. For example, the user may be instructed to run in place(or jump, dance, cycle, etc.) for a short time interval (e.g., a fewseconds, etc.). The processor(s) 14 then determines the quality of thesignal(s) from the sensor(s) of the biometric monitoring device 10during the quality test regimen (Block 110). Additional operationsassociated with determining the signal quality may include determiningif the biometric monitoring device 10 is being worn by the user anddetermining if the user is actually following the quality test regimen(i.e., running in place, etc.) by checking readings from one or moresensors in the device, such as a PPG sensor and/or an accelerometerassociated with the biometric monitoring device 10.

Next, the processor(s) 14 determines what information to communicate tothe wearer of the biometric monitoring device 10 (Block 120) andcommunicates this information to the user (Block 130). For example, theprocessor(s) determines whether the fit of the biometric monitoringdevice 10 is okay or whether the user needs to readjust the biometricmonitoring device 10 in some way, and this information is communicatedto the user. For example, if the biometric monitoring device 10 ispositioned correctly (i.e., sensor signal quality is acceptable), theprocessor(s) 14 communicates this information to the user. If thebiometric monitoring device 10 is not positioned correctly (i.e., sensorsignal quality is not acceptable), the processor(s) 14 communicates thisinformation to the user.

Information communicated to the user may be audibly and/or visuallycommunicated. For example, FIGS. 5A-5B, 6A-6B, 7A-7B, and 8A-8Billustrate various visual communications to a user regarding whether thefit of the biometric monitoring device 10 is okay or whether the userneeds to readjust the biometric monitoring device 10 in some way. Thesevisual communications may be made to the user via the wearable biometricmonitoring device 10 and/or via a remote device 40 (e.g., a smartphone,computer, etc.) in communication with the biometric monitoring device10.

FIG. 5A illustrates a communication sent to a user in the form of agraphical illustration 200 that a biometric monitoring device 10 in theform of an earbud is positioned inside the ear of the user at anincorrect angle. FIG. 5B illustrates a communication sent to a user inthe form of a graphical illustration 202 that a biometric monitoringdevice 10 in the form of an earbud is positioned inside the ear of theuser at the correct angle.

FIG. 6A illustrates a communication sent to a user in the form of agraphical illustration 204 that a biometric monitoring device 10 in theform of an earbud is positioned outside the ear of the user at anincorrect angle. FIG. 6B illustrates a communication sent to a user inthe form of a graphical illustration 206 that a biometric monitoringdevice 10 in the form of an earbud is positioned outside the ear of theuser at the correct angle.

FIG. 7A illustrates a communication sent to a user in the form of agraphical illustration 208 a that a biometric monitoring device 10 inthe form of an earbud is positioned correctly within the ear of a user.FIG. 7B illustrates a communication sent to a user in the form of agraphical illustration 208 b that a biometric monitoring device 10 inthe form of an earbud is positioned at an incorrect angle within the earof the user.

FIGS. 8A and 8B illustrate communications sent to a user in the form ofgraphical illustrations 210 a, 210 b with instructions for properlypositioning a biometric monitoring device 10 in the form of an earbudwithin the ear of the user. For example, the graphical illustration 210a of FIG. 8A instructs the user to place the earbud inside the ear, andthe graphical illustration 210 b of FIG. 8B instructs the user to rotatethe earbud forward such that the earbud arm fits between the intertragicnotch and “locks” the sensor region of the earbud inside the ear.

The aforementioned examples of audio-visual communication to the userare illustrative only and not meant to be limiting. For example, if aPPG sensor is located at the tip of an earbud, and if the signal qualityfrom that PPG sensor is deemed to be insufficient, the visualpresentation to the user may suggest that the subject place the ear tipdeeper in the ear, change the ear tip gel, rotate the tip, etc.Similarly, for the case of a band, such a wristband, comprising a PPGsensor, the visual presentation may suggest that the subject tighten theband, change the band fitting, rotate the band, etc.

Referring now to FIG. 9, a time-domain PPG waveform 300 is illustrated.The AC component (“Peak AC”) represents the pulsatile component of thephotoplethysmogram, the component related to blood flow, and the DCcomponent (S_(DC)) represents the non-pulsatile component. Theillustrated waveform 300 is from the sensor(s) 12 within a biometricmonitoring device 10 worn by a user and illustrates the condition wherethere are negligible motion artifacts associated with the waveform as aresult of user motion, footsteps, breathing, etc. The illustratedwaveform 300 illustrates two heartbeats of the user. FIG. 10 illustratesa time-domain PPG waveform 302 from the sensor(s) 12 within a biometricmonitoring device 10 worn by a user where there are substantial motionartifacts. The actual heart beat waveform 300 is illustrated in dottedline and is masked by the substantial motion artifacts.

FIG. 11A illustrates an exemplary spectrogram 400 of a PPG signal from auser wearing a biometric monitoring device 10 and without active motionnoise removal. A(ω) refers to the spectral amplitude of the PPG signalat a frequency ω. The spectrogram 400 includes, not only heart rate (HR)signals 402, but also motion signals from body motion 404 and motionsignals from user breathing 406. In FIG. 11B, active motion noiseremoval via one or more filters associated with the processor(s) 14 haveremoved the motion signals from body motion 404 and motion signals fromuser breathing 406. Noise attenuation and removal is described in detailin U.S. Pat. Nos. 8,157,730, 8,251,903, 8,652,040, 8,647,270, 8,700,111,U.S. Provisional Patent Application No. 61/750,490, PCT ApplicationPublication No. WO 2013/109389, PCT Application Publication No. WO2013/109390, and PCT Application Publication No. WO 2013/019494, whichare incorporated herein by reference in their entireties.

If multiple wavelengths are used in PPG sensing, such as the case forpulse oximetry, then there will be multiple spectrograms with similarspectral profiles, and the ratio of functions of A(ω_(HR))_(λ1) withrespect to functions of A(ω_(HR))_(λ2) . . . A(ω_(HR))_(λn) may beproportional to the concentration of various types of hemoglobin in theblood. For example, the SpO₂ concentration of blood may be proportionalto A(ω_(HR))_(λ1)/A(ω_(HR))_(λ2) (where λ1 may be a shorter wavelengththan λ2) or a ratio of functions of these spectral amplitudes.

According to some embodiments of the present invention, signal qualityfrom a sensor in the biometric monitoring device 10 can be determined bythe following equation:

${{Signal}\mspace{14mu}{Quality}} = {Q_{S}\frac{\alpha\left( {A\left( \omega_{HR} \right)} \right)}{\sum{A\left( \omega_{i} \right)}}}$

In other words, signal quality Qs is proportional to heart rate signaldivided by the sum of the signal components (the sum of all spectralamplitudes for all of the “n” discrete frequencies ω_(i), for i=0 to n)associated with user motion. This formula may be useful once aspectrogram is generated for a PPG signal collected by the sensor(s) 12.In the spectral domain, the signal quality Qs may be expressed as aratio of the spectral amplitude at the HR (heart rate) frequency dividedby a sum of various other spectral amplitudes that may also exist in thespectrogram. Similarly, the signal quality Qs may be related to a ratioof functions of various spectral amplitudes. The signal quality Qs maybe assessed either before or after motion-artifact removal, but in thecase of assessing signal quality post-motion-artifact removal, the sumof spectral amplitudes in the denominator is likely to be smaller thanfor the case of assessing signal quality pre-motion-artifact removal, assuggested by FIG. 11A and FIG. 11B. Thus, when there are less spectralartifacts from motion artifacts and other unwanted time-varyingartifacts, the signal quality Qs is likely to be higher than for thecase where many such artifacts are present.

The formula for assessing signal quality is not meant to be limiting.Various other formulas or methods may be used to assess signal qualityaccording to embodiments of the present invention. For example, adifferent, simpler formula may assess only the magnitude of the spectralamplitude at the HR frequency. A different, more complicated formula mayassess the ratio of 2 or more signal qualities, as calculated from theabove formula for multiple PPG wavelengths. Additionally, a time-domainapproach may be preferred in some embodiments. For example, the peakamplitude of a time-domain photoplethysmogram (such the PeakAC as shownin FIG. 9) may be calculated and used to assess signal quality, withhigher PeakAC correlating with higher Qs. In the time domain, a morecomplicated method may be to assess Qs by the ratio: PeakAC/PeakCadence,where PeakCadence refers to the peak amplitude of an accelerometeroutput at the user's cadence (step rate, jumping rate, cycling rate,etc.). Additionally, the spectral and time-domain approaches describedabove for a PPG sensor may also be applied for the case where thesensors comprise an ECG sensor (such as that used in ECG leads, ECGchest straps or ECG wristbands) and an inertial sensor (such as anaccelerometer).

Embodiments of the present invention may also be applied for measuringPPG from the ear canal, body temperature from the tympanic membrane, orvarious other biometrics at various locations of the body. For the caseof an in-ear measurement, a user may be instructed via the wearablemonitor 10 and/or remote device 40 to wiggle their ear or talk while PPGor body temperature is being measured from the ear canal or tympanicmembrane. The signal quality Qs may then be calculated for theseactivities to make certain the signal quality is above the minimumacceptable value. In contrast, a user measuring PPG and skin temperatureat the leg may be instructed to stomp the leg while signal quality Qs isbeing assessed.

The processor(s) 14 may also determine that the position of the wearabledevice 10 is not sufficient for accurate biometric monitoring. Forexample, the processor(s) 14 may process signal readings from anaccelerometer 12 to determine if the wearable device 10 is being worncorrectly with respect to the ground. One methodology for determiningthe location of ground via an accelerometer may be to assess three (3)or more axes of the accelerometer to determine the vector of 1 G offorce, using commonly known trigonometric methods, and then to assesswhether that vector is sufficiently aligned with a desired vector forthat 1 G of force. For example, it may be desired that an accelerometerplaced in an earbud have ˜1 G of force directly along the Z-vector. Ifthe location of the 1 G of force is found by the processor(s) 14 to beclose enough to the axis along the Z-vector, then the processor(s) 14may determine that the earbud is being worn properly (FIGS. 5B, 6B, &7A); otherwise, the processor(s) 14 may determine that the earbud is notbeing worn properly (FIGS. 5A, 6A, & 7B). In some cases, determiningthat the subject is wearing the monitoring device 10 appropriately mayinvolve audio-visual feedback from the monitoring device 10 and/or aremote device 40. For example, a prompt may notify a user to wear awristband with the sensor region 34 located at a certain spot along thewrist. The processor(s) 14 may then determine whether the device isbeing worn appropriately in accordance with the audio-visualinstructions.

FIG. 12 is an exemplary plot 500 of signal quality Qs (as may bedetermined by methods described earlier as well as other methods) overtime for an exemplary run by a user wearing a biometric monitoringdevice 10. The dotted line 502 represents the minimum signal quality Qsthat is acceptable for producing accurate physiological information. Thesignal quality Qs may dip below the minimum acceptable quality duringcertain aspects of the run, such as during erratic running motion orhigh ambient light interference. The processor(s) 14 may determine thatthe signal quality Qs is too low for the measured biometric to beaccurate, preventing the errant biometric reading from being appliedtowards a physiological assessment. For example, post analysis on therunning data generated by the sensor(s) 12 in the wearable biometricmonitoring device 10 may be generated by processing biometrics (heartrate, breathing rate, R-R interval, HRV, blood pressure, etc.) andactivity metrics (cadence, speed, pace, distance, total steps, etc.)together. As a specific example, by processing heart rate and cadencetogether over time, the processor(s) 14 may determine when the user hasstarted and stopped an exercise and may then determine the user's heartrate recovery (the change in heart rate from the stop of exercise and 1minute or more following the stop of exercise). But, if the signalquality Qs is determined to be below the minimum allowable level ofquality during the relevant data collection period (in this case theperiod between the stop of the exercise and a minute or moreafterwards), then the processor(s) 14 may determine that heart raterecovery cannot be accurately determined. Alternatively, theprocessor(s) 14 may estimate the heart rate recovery with the errantdata along with an assessment that the confidence of the estimation ispoor, and both of the estimate and signal quality may be reported to theuser audio-visually.

FIG. 13 is a plot 500 of signal quality Qs over time for a userconducting a self-fitting test, according to some embodiments of thepresent invention. The dotted line 502 represents the minimum signalquality Qs that is acceptable for producing accurate physiologicalinformation. The illustrated plot 500 can be presented to the user via adisplay in a remote device 40 in communication with the biometricmonitoring device 10.

At time t₀, the user attaches the biometric monitoring device 10 tohis/her body (e.g., inserts earbud sensor module in ear, straps on awristband sensor module, etc.) and turns the biometric monitoring deviceon. At time t_(a), the user receives the first indication of signalquality and, as illustrated, it is below the minimum signal quality line502. At time t_(b), the user makes one or more adjustments to thebiometric monitoring device at this point to improve the signal quality.For example, the user repositions the biometric monitoring device 10relative to the body, adds a spacer or gel device to create a better fitrelative to the body, tightens a strap securing the biometric monitoringdevice 10 to the body, etc. As a result of the user adjustment, thesignal quality improves as illustrated between time t_(b) and timet_(c).

However, as illustrated in FIG. 13, the user continues to adjust thebiometric monitoring device 10 and the signal quality falls below theminimum signal quality line 502 by time t_(d). The user continuesadjustment of the biometric monitoring device until the signal qualityis raised above the minimum signal quality line 502. At time t_(e), thesignal quality is acceptable and the user keeps the biometric monitoringdevice 10 in place.

FIG. 14 is a plot 500 of signal quality Qs over time for a userconducting an instructed “quick test” regimen, according to someembodiments of the present invention. The dotted line 502 represents theminimum signal quality Qs that is acceptable for producing accuratephysiological information. The illustrated plot 500 can be presented tothe user via a display in a remote device 40 in communication with thebiometric monitoring device 10.

At time t₀, the user attaches the biometric monitoring device 10 tohis/her body (e.g., inserts earbud sensor module in ear, straps on awristband sensor module, etc.) and turns the biometric monitoring deviceon. At time t_(a), the user is instructed to execute a “quick test”regimen, such as running in place for a short period of time (e.g., 15seconds, etc.). As illustrated, between the time t_(a) and t_(b), thesignal quality is above the minimum line 502, and this is because theuser is at rest and motion artifacts are not impairing the signal 500.However, when the user starts the “quick test” regimen (i.e., running inplace), the signal quality falls below the minimum signal quality line502 at time t_(c). The user is then instructed to adjust the biometricmonitoring device 10 (e.g., reposition the biometric monitoring device10 relative to the body, add a spacer or gel device to create a betterfit relative to the body, tighten a strap securing the biometricmonitoring device 10 to the body, etc.) and repeat the “quick test”regimen (e.g., running in place). As a result of the user adjustment,the signal quality improves as illustrated at times t_(d) and t_(e). Thesignal quality remains above the minimum signal quality line 502 at timet_(f) after the “quick test” regimen.

The methods described for determining the quality of a biometric signalmay be applied towards facilitating accurate autonomous biometricassessments by assuring the utilization of only highly accurate data.For example, as summarized in FIG. 15, for a subject donning a wearablesensor device 10, the device 10 may collect sensor data (Block 600),determine the biometric signal quality (Block 610), record the biometricdata (comprising both sensor data and biometric signal quality data)(Block 620), and then mark (i.e., label or identify) biometric datareadings that have sufficient and/or insufficient quality for anaccurate assessment (Block 630). Once sufficient quality data isidentified and isolated from data having insufficient data quality, thisquality data may be used to generate an accurate assessment for thesubject (Block 640). As a specific example, a fitness assessment for thesubject may be generated by assessing only those biometric data pointsthat are found to have sufficient quality for the assessment. Thebiometric data that may be assessed for quality using the method of FIG.15 may include not only vital sign biometrics (e.g., subject heart rate,subject blood pressure, subject temperature, subject respiration rate,and/or subject perspiration rate, etc.), but also contextual biometrics(biometric assessments), such as whether a person is breathingsufficiently, whether a device is being worn correctly, or the like. Aspecific example of applying the method illustrated in FIG. 15 towards adetermination of a device “being worn” is illustrated in FIGS. 19 and20, described below.

As another example, an accurate fitness assessment may require inputtingmore vetted data points into the assessment model than those that havebeen vetted by the wearable device 10 to be of sufficient quality. Insuch case, the fitness assessment may be generated by factoring bothtypes of data points (e.g., data points having sufficient signal quality(vetted data points) and data points not having sufficient quality) byextrapolating or otherwise interpolating between data points that aremarked as having sufficient data quality. A specific example of this ispresented in FIG. 16 for the case of determining heart rate recovery forheart rate data having both high quality and low quality sensor signals.

In FIG. 16, plot 700 represents high quality sensor signals and plot 710represents low quality sensor signals. In FIG. 16, the dotted line 702is interpolated between two regions of high quality data, where thesignal confidence is above the 100% confidence threshold. Note that forthis heart rate recovery assessment to be completely autonomous,contextual information may be required to be known, such as whether theperson was exercising for a sufficient period of time, if they were atan elevated heart rate or exertion level, and/or when the person stoppedexercising. This type of contextual information may be input by the uservia a user interface or provided by processing data from anaccelerometer or other motion-tracking device that is integrated withinthe wearable device 10.

It should also be noted that, for the example of FIG. 16, an alternativemethod of generating the fitness assessment may be to generate anassessment plus assessment confidence using all data points, whetherthey be marked as sufficient or insufficient in quality. In this case,an interpolation may not be required, as all data points are used tomake the assessment but a “score” or “confidence” of the overallassessment (in this example a heart rate recovery assessment) isgenerated by weighting high- and low-quality data points. In a specificexample of this, if 20% of the data points required to generate afitness assessment have been found to be of insufficient signal quality,then the assessment may be calculated using 100% of the data points butwith a note that the assessment confidence score is less than 100%.Additionally, if an assessment is taken over the course of time, then anotification may be given to the user when the assessment was calculatedwith 100% confidence and less than 100% confidence, such as the case forFIG. 17, where a plot 800 of HRR (Heart Rate Recovery) vs. time ispresented for a user along with information about the confidence of thatHRR score for each data point. For example, data points 802 represent100% confidence in HRR and data points 804 represent less than 100%confidence in HRR.

The methods described for determining the quality of a biometric signalmay be applied towards facilitating accurate autonomous biometricassessments by assuring the utilization of data collected only when adevice 10 is being worn. For example, a wearable sensor device 10 maycomprise sensors 12 that cannot innately determine if the sensor device10 is being worn by the subject. Thus, without an intelligent way ofdetermining that a sensor device 10 is being worn, erroneous assessmentsmay be generated. As a specific example, a person wearing an opticalheart rate monitor may find that removing the monitor from the body andplacing that monitor on a counter yields erroneous heart rate readings,perhaps due to the device picking up ambient light flicker and/orscattered light from table vibrations. In such case, an assessment ofthat subject's average heart rate for the day, or calories burned forthe day, may be wildly skewed. A method of addressing this problem issummarized in FIG. 18.

In FIG. 18, a wearable device 10 having a sensor 12 may collect sensordata from a user (Block 900), determine if the sensor 12 is being worn(Block 910), and record the biometric data (comprising both sensor dataand biometric signal quality data) (Block 920). The wearable device 10may be programmed to mark when the biometric data readings are found tobe associated with the device 10 not being worn (Block 930), such as thecase for very low signal quality. Then an accurate assessment may begenerated for the subject by factoring (weighting) sensor readingsmarked for periods where the wearable device 10 was determined to beworn (Block 940), as described above with respect to FIG. 16. Techniquesfor determining whether a device 10 is being worn by the user have beendescribed above; however, an additional technique may comprise averagingmotion signals collected by a motion sensor, such as an accelerometer,over a period of time to determine whether the average motion is highenough to be classified as “being worn” motion.

A variety of methods may be used to determine whether a device 10 isbeing worn. For example, the signal quality may be assessed byprocessing sensor readings to see if they are within an acceptablerange. For example, the output of a photodetector in a wearable PPGsensor may be processed to determine if the DC (i.e., the DC componentof a PPG signal) background is above or below a certain thresholdassociated with being worn or not worn. In many optical configurations,the DC background will be lower than a certain threshold when a PPGsensor is away from the body.

Additionally, the output of a PPG sensor may be processed by a processorfor determining heart rate, breathing rate, and/or other vital sign(s)and then determining if the vital sign(s) is reasonable for a humanbeing (in general), based on a record of normative data for humans orhumans of a certain characteristic (gender, age, habitus, demographic,etc.), or if the vital sign(s) is reasonable for a given subject basedon a personalized record collected for that subject.

Referring to FIG. 19, a process for detecting that a biometricmonitoring device 10 is being worn by a subject is illustrated. Thebiometric monitoring device 10 includes a PPG sensor and the methodincludes collecting PPG sensor data (Block 1000), processing the PPGdata (Block 1010), determining a “being worn status” using the processedPPG data (Block 1020), and generating an indicator or flag regarding the“being worn status” of the monitoring device (Block 1030). Processingthe PPG data (Block 1010) may include determining if DC intensity (i.e.,the intensity of the DC component of the PPG sensor signal) is within areasonable range, determining the presence (i.e., existence) of a vitalsign (subject heart rate, subject blood pressure, subject temperature,subject respiration rate, and/or subject perspiration rate, etc.),determining if heart rate value (HRV) is within a reasonable range, etc.The flag is used to indicate if the monitoring device 10 is being wornby the subject or not being worn by the subject (i.e., the “being wornstatus”).

Referring to FIG. 20, a process for detecting that a biometricmonitoring device 10 is being worn by a subject is illustrated. Thebiometric monitoring device 10 includes a PPG sensor and the methodincludes collecting PPG sensor data and/or inertial data via a motionsensor associated with the monitoring device 10 (Block 1100). The signalquality of the PPG sensor data and/or inertial data is determined viaprocessing (Block 1110) and an estimate is made as to whether themonitoring device 10 is being worn via the processed PPG sensor dataand/or inertial data (Block 1120). A “being worn status” is determinedby processing the estimate as to whether the monitoring device 10 isbeing worn in context with the signal quality of the PPG sensor dataand/or inertial data (Block 1130). An indicator or flag is thengenerated regarding the “being worn status” of the monitoring device(Block 1140). The flag is used to indicate if the monitoring device 10is being worn by the subject or not being worn by the subject (i.e.,“being worn status”).

In another embodiment for determining if a device 10 is being worn, aPPG sensor 12, and/or a processor 14 in communication with the PPGsensor, may be configured to generate RRi (R-R interval) and or heartrate variability (HRV) information, and this information can beprocessed to determine if the statistics of consecutive R-R intervals(the time intervals, NN, between consecutive ECG or PPG peaks) isconsistent with a living person (or not). This innovation can beparticularly important for the aforementioned case where table or floorvibrations may generate a false signal in a PPG sensor, emulating heartrate. In such case, the processor may confuse consecutive peaks fromoptical scatter signals caused by table vibrations as a real live heartrate, and thus generate a false signal (or flag) that the device isbeing worn. But, by running statistics through the time intervalsbetween consecutive peaks, the processor 14 may be able to identifyhuman heart rate variability from inanimate (i.e., table)vibration-caused peaks in the optical scatter signal of a PPG sensor.This is because false RRi peaks (from mechanical vibrations or externaloptical noise) are not likely to have the same statistical distributionas that of true RRi peaks (from a human ECG or PPG signals). Forexample, vibration noise or flickering noise from mechanical or opticalnoise sources will often generate false peaks characterized by highregularity in time (higher than that of real human-caused peaks),yielding a low STDEV (standard deviation) between successive NNs. Thetypes of statistics on consecutive time-intervals between peaks maycomprise (but are not limited to): 1) SDNN (standard deviation betweenconsecutive time-intervals (NN)), 2) pNN50 (the percentage of successiveNNs that differ by more than 50 ms (milliseconds) divided by the totalnumber of NNs during a given time period), 3) RMSSD (the “root meansquare of successive differences” between adjacent NNs, 4) PSD (powerspectral density) or PSD ratios for high and low frequency ranges, 5)and the like.

Similarly, the output of a temperature sensor, such as a tympanictemperature sensor or skin sensor or the like, may be processed todetermine if the temperature is above or below a certain thresholdassociated with being worn. In this case, the processor 14 may look fora temperature reading within a reasonable range associated withhuman-generated heat; being inside that range may trigger a “flag” (suchas a 1 or 0 bit) that the device is being worn, whereas outside thatrange may change the flag to not being worn. In general, such a “beingworn flag” may be generated for any determination by a processor thatthe device is being worn (or not worn), regardless of the sensortransduction mechanism (i.e., using the method provided in FIG. 19). Insome embodiments, the output of a motion sensor may also be used todetermine if a wearable device 10 is being worn. For example, the outputof an accelerometer may be processed to assess micro-motions associatedwith a human subject at rest or processed to assess larger motionsassociated with gross body motion. The presence of at least sufficientmicro-motions may be used to generate a “flag” that the device 10 isbeing worn.

It should be noted that multiple of the methods described herein forassessing whether a device 10 is being worn may be used in combinationin order to improve the assessment of a device 10 being worn. As aparticular example of this combinational method, a processor 14 mayfactor both the DC intensity of a PPG signal and the HRV of the PPGsignal to determine if the device 10 is being worn. More specifically,if the DC intensity from a detector output in a PPG sensor is below (orabove) a DC threshold intensity, and if the HRV determined from thedetector output of a PPG signal shows an HRV below (or above) an HRVthreshold intensity, then the processor may determine that the device 10is not being worn, generating a “flag”. It should be noted that when thedevice is not being worn, the HRV assessment would be performed on PPGdata that is not truly PPG data but is rather optical noise detectedfrom a non-worn device. Nonetheless, the term “HRV” is used here torepresent the statistical analysis performed on the detector output.

Because sensor readings may be slightly different for different subjectsin the case of a wearable device 10 being worn/not-worn, the generationof an accurate “being worn flag” may require that the processor 14 betrained for that particular subject. For example, the subject may beasked to input, via a user interface, when they are wearing the wearabledevice 10 and when they are not wearing the wearable device 10. Theprocessor 14 may then assess sensor readings to determine the unique“signature” of that subject for the device 10 being worn and/or for thedevice 10 being not worn. This signature may be programmed andassociated with the wearable device 10 such that a more accuratedetermination of being worn/not worn may be generated in the future forthat subject.

Example embodiments are described herein with reference to blockdiagrams and flow diagrams. It is understood that a block of the blockdiagrams and flow diagrams, and combinations of blocks in the blockdiagrams and flow diagrams, can be implemented by computer programinstructions that are performed by one or more computer circuits, suchas electrical circuits having analog and/or digital elements. Thesecomputer program instructions may be provided to a processor circuit ofa general purpose computer circuit, special purpose computer circuit,and/or other programmable data processing circuit to produce a machine,such that the instructions, which execute via the processor of thecomputer and/or other programmable data processing apparatus, transformand control transistors, values stored in memory locations, and otherhardware components within such circuitry to implement thefunctions/acts specified in the block diagrams and flow diagrams, andthereby create means (functionality) and/or structure for implementingthe functions/acts specified in the block diagrams and flow diagrams.

These computer program instructions may also be stored in a tangiblecomputer-readable medium that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instructions whichimplement the functions/acts specified in the block diagrams and flowdiagrams.

A tangible, non-transitory computer-readable medium may include anelectronic, magnetic, optical, electromagnetic, or semiconductor datastorage system, apparatus, or device. More specific examples of thecomputer-readable medium would include the following: a portablecomputer diskette, a random access memory (RAM) circuit, a read-onlymemory (ROM) circuit, an erasable programmable read-only memory (EPROMor Flash memory) circuit, a portable compact disc read-only memory(CD-ROM), and a portable digital video disc read-only memory(DVD/BlueRay).

The computer program instructions may also be loaded onto a computerand/or other programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer and/or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions which execute on the computer or otherprogrammable apparatus provide steps for implementing the functions/actsspecified in the block diagrams and flow diagrams. Accordingly,embodiments of the present invention may be embodied in hardware and/orin software (including firmware, resident software, micro-code, etc.)that runs on a processor such as a digital signal processor, which maycollectively be referred to as “circuitry,” “a module” or variantsthereof.

It should also be noted that the functionality of a given block of theblock diagrams and flow diagrams may be separated into multiple blocksand/or the functionality of two or more blocks of the block diagrams andflow diagrams may be at least partially integrated. Finally, otherblocks may be added/inserted between the blocks that are illustrated.Moreover, although some of the diagrams include arrows on communicationpaths to show a primary direction of communication, it is to beunderstood that communication may occur in the opposite direction to thedepicted arrows.

The foregoing is illustrative of the present invention and is not to beconstrued as limiting thereof. Although a few exemplary embodiments ofthis invention have been described, those skilled in the art willreadily appreciate that many modifications are possible in the exemplaryembodiments without materially departing from the teachings andadvantages of this invention. Accordingly, all such modifications areintended to be included within the scope of this invention as defined inthe claims. The invention is defined by the following claims, withequivalents of the claims to be included therein.

That which is claimed is:
 1. A method of detecting if a biometricmonitoring device is being worn by a subject, wherein the biometricmonitoring device includes a PPG sensor, the method comprising:receiving a signal produced by the PPG sensor; processing the signalproduced by the PPG sensor via at least one processor to determine astatistical distribution of a group of consecutive peaks in the signal;determining via the at least one processor that the device is being wornif the statistical distribution of the group of consecutive peaks in thesignal is consistent with R-R intervals of a living person or that thedevice is not being worn if the statistical distribution of the group ofconsecutive peaks in the signal is not consistent with R-R intervals ofa living person; and autonomously adjusting an adjustment mechanism ofthe biometric monitoring device responsive to the determination that thedevice is being worn.
 2. The method of claim 1, further comprisinggenerating, via the at least one processor, an indication as to whetheror not the biometric monitoring device is being worn by the subjectresponsive to whether or not the statistical distribution of the groupof consecutive peaks in the signal is consistent with R-R intervals of aliving person.
 3. The method of claim 2, wherein the indicationcomprises a flag.
 4. The method of claim 1, wherein the biometricmonitoring device is integrated within an earbud, an audio headset, awrist strap, a wrist watch, an ankle bracelet, or an armband.
 5. Themethod of claim 1, wherein the biometric monitoring device comprises aband configured to at least partially encircle a portion of the body ofa subject, and wherein the portion of the body comprises a limb, a nose,an earlobe, and/or a digit.
 6. A method of generating a physiologicalassessment of a subject wearing a biometric monitoring device, whereinthe biometric monitoring device includes a PPG sensor, the methodcomprising: receiving a signal produced by the PPG sensor comprising anAC component and a DC component; processing the signal produced by thePPG sensor via at least one processor to determine an intensity of theDC component of the signal produced by the PPG sensor; determining viathe at least one processor that the device is being worn if theintensity of the DC component of the signal produced by the PPG sensoris within a first predetermined range; and generating the physiologicalassessment of the subject in which first data associated with the signalreceived from the PPG sensor while the device is being worn is given agreater weight in generating the physiological assessment than seconddata associated with the signal received from the PPG sensor while thedevice is not being worn.
 7. The method of claim 6, further comprisinggenerating, via the at least one processor, an indication as to whetheror not the biometric monitoring device is being worn by the subjectresponsive to whether or not the intensity of the DC component of thesignal produced by the PPG sensor is within the first predeterminedrange.
 8. The method of claim 7, wherein the indication comprises aflag.
 9. The method of claim 6, wherein the biometric monitoring deviceis integrated within an earbud, an audio headset, a wrist strap, a wristwatch, an ankle bracelet, or an armband.
 10. The method of claim 6,wherein the biometric monitoring device comprises a band configured toat least partially encircle a portion of the body of a subject, andwherein the portion of the body comprises a limb, a nose, an earlobe,and/or a digit.
 11. The method of claim 6, further comprisingautonomously adjusting an adjustment mechanism of the biometricmonitoring device responsive to determining that the device is beingworn.
 12. The method of claim 1, wherein the adjustment mechanism isconfigured to facilitate proper placement of the biometric monitoringdevice relative to the body of the subject.
 13. The method of claim 1,wherein the adjustment mechanism comprises an actuator.
 14. A method ofgenerating a physiological assessment of a subject wearing a biometricmonitoring device, wherein the biometric monitoring device includes aPPG sensor, the method comprising: receiving a signal produced by thePPG sensor; processing the signal produced by the PPG sensor via atleast one processor to determine a statistical distribution of a groupof consecutive peaks in the signal; determining via the at least oneprocessor that the device is not being worn if the statisticaldistribution of the group of consecutive peaks in the signal is notconsistent with R-R intervals of a living person; and generating thephysiological assessment of the subject in which first data associatedwith the signal produced by the PPG sensor while the device is not beingworn is not included or is provided a lesser weight in generating thephysiological assessment responsive to the determination that the deviceis not being worn.
 15. The method of claim 14, wherein the signal is afirst signal, and further comprising: receiving a second signal producedby the PPG sensor; and determining via the at least one processor thatthe device is being worn if a statistical distribution of the group ofconsecutive peaks in the second signal is consistent with R-R intervalsof a living person, wherein, while generating the physiologicalassessment of the subject, second data associated with the second signalproduced by the PPG sensor while the device is being worn is given agreater weight than the first data associated with the first signalproduced by the PPG sensor while the device is not being worn.
 16. Themethod of claim 14, wherein receiving the signal produced by the PPGsensor comprises receiving the signal while the device is not being wornby the subject.
 17. The method of claim 1, wherein determining via theat least one processor that the device is being worn further comprisesdistinguishing the R-R intervals of the living person from opticalscatter signals received by the PPG sensor using the statisticaldistribution of the group of consecutive peaks in the signal.
 18. Themethod of claim 14, wherein determining via the at least one processorthat the device is being worn further comprises distinguishing the R-Rintervals of the living person from optical scatter signals received bythe PPG sensor using the statistical distribution of the group ofconsecutive peaks in the signal.
 19. The method of claim 1, wherein thestatistical distribution comprises a first calculation of a firstpercentage of the successive time-intervals between respective ones ofthe group of consecutive peaks that differ by more than 50 ms(milliseconds) divided by a total number of the successivetime-intervals during a given time period and/or a second calculation ofa power spectral density for high and low frequency ranges of the groupof consecutive peaks in the signal.
 20. The method of claim 14, whereinthe statistical distribution comprises a first calculation of a firstpercentage of the successive time-intervals between respective ones ofthe group of consecutive peaks that differ by more than 50 ms(milliseconds) divided by a total number of the successivetime-intervals during a given time period and/or a second calculation ofa power spectral density for high and low frequency ranges of the groupof consecutive peaks in the signal.